docs: def calc_influence_arrays_for_abc(model_obj,
                                    mle_est,
                                    init_values,
                                    epsilon,
                                    **fit_kwargs):
      """
      Calculates the empirical influence array and the 2nd order empirical
      influence array needed to compute the approximate boostrap confidence (ABC)
      intervals.
  
      Parameters
      ----------
      model_obj : an instance or sublcass of the MNDC class.
          Should be the model object that corresponds to the model we are
          constructing the bootstrap confidence intervals for.
      mle_est : 1D ndarray.
          Should contain the desired model's maximum likelihood point estimate.
      init_vals : 1D ndarray.
          The initial values used to estimate the desired choice model.
      epsilon : positive float.
          Should denote the 'very small' value being used to calculate the
          desired finite difference approximations to the various influence
          functions. Should be 'close' to zero.
      fit_kwargs : additional keyword arguments, optional.
          Should contain any additional kwargs used to alter the default behavior
          of `model_obj.fit_mle` and thereby enforce conformity with how the MLE
          was obtained. Will be passed directly to `model_obj.fit_mle`.
  
      Returns
      -------
      empirical_influence : 2D ndarray.
          Should have one row for each observation. Should have one column for
          each parameter in the parameter vector being estimated. Elements should
          denote the empirical influence of the associated observation on the
          associated parameter.
      second_order_influence : 2D ndarray.
          Should have one row for each observation. Should have one column for
          each parameter in the parameter vector being estimated. Elements should
          denote the second order empirical influence of the associated
          observation on the associated parameter.
  
      References
      ----------
      Efron, Bradley, and Robert J. Tibshirani. An Introduction to the Bootstrap.
          CRC press, 1994. Section 22.6, Equations 22.32 and 22.36.
      """
      # Calculate the two arrays of finite difference terms needed for the
      # various influence arrays that we want to calculate.
      term_plus, term_minus = calc_finite_diff_terms_for_abc(model_obj,
                                                             mle_est,
                                                             init_values,
                                                             epsilon,
                                                             **fit_kwargs)
      # Calculate the empirical influence array.
      empirical_influence =\
          calc_empirical_influence_abc(term_plus, term_minus, epsilon)
      # Calculate the second order influence array.
      second_order_influence =\
          calc_2nd_order_influence_abc(mle_est, term_plus, term_minus, epsilon)
      # Return the desired terms
      return empirical_influence, second_order_influence